Development of the Speech-to-Text Chatbot Interface Based on Google API

被引:0
|
作者
Shakhovska, Nataliya [1 ]
Basystiuk, Oleh [1 ]
Shakhovska, Khrystyna [1 ]
机构
[1] Lviv Polytech Natl Univ, UA-79013 Lvov, Ukraine
来源
MOMLET&DS-2019: MODERN MACHINE LEARNING TECHNOLOGIES AND DATA SCIENCE | 2019年 / 2386卷
关键词
natural language processing; speech-to-text; Google API; !text type='Python']Python[!/text; Flask; chatbot; hashing; time complexity; prefix-function;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The paper describes possibilities, which are provided by open APIs, and how to use them for creating unified interfaces using the example of our bot based on Google API. In last decade AI technologies became widespread and easy to implement and use. One of the most perspective technology in the AI field is speech recognition as part of natural language processing. New speech recognition technologies and methods will become a central part of future life because they save a lot of communication time, replacing common texting with voice/audio. In addition, this paper explores the advantages and disadvantages of well- known chatbots. The method of their improvement is built. The algorithms of Rabin-Karp and Knut-Pratt are used. The time complexity of proposed algorithm is compared with existed one.
引用
收藏
页码:212 / 221
页数:10
相关论文
共 50 条
  • [1] Speech-to-text and speech-to-speech summarization of spontaneous speech
    Furui, S
    Kikuchi, T
    Shinnaka, Y
    Hori, C
    IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, 2004, 12 (04): : 401 - 408
  • [2] Elpis, an accessible speech-to-text tool
    Foley, Ben
    Rakhi, Alina
    Lambourne, Nicholas
    Buckeridge, Nicholas
    Wiles, Janet
    INTERSPEECH 2019, 2019, : 4624 - 4625
  • [3] Application of Extractive Text Summarization Algorithms to Speech-to-Text Media
    Victor, Dominguez M.
    Eduardo, Fidalgo F.
    Biswas, Rubel
    Alegre, Enrique
    Fernandez-Robles, Laura
    HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2019, 2019, 11734 : 540 - 550
  • [4] Establishing a Baseline of Romanian Speech-to-Text Models
    Ungureanu, Dan
    Badeanu, Madalina
    Marica, Gabriela-Catalina
    Dascalu, Mihai
    Tufis, Dan Ioan
    2021 INTERNATIONAL CONFERENCE ON SPEECH TECHNOLOGY AND HUMAN-COMPUTER DIALOGUE (SPED), 2021, : 132 - 138
  • [5] A Survey on Bengali Speech-to-Text Recognition Techniques
    Sultana, Rumia
    Palit, Ratesh
    2014 9TH INTERNATIONAL FORUM ON STRATEGIC TECHNOLOGY (IFOST), 2014, : 26 - 29
  • [6] Semantic MIMO Systems for Speech-to-Text Transmission
    Weng, Zhenzi
    Qin, Zhijin
    Xie, Huiqiang
    Tao, Xiaoming
    Letaief, Khaled B.
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 23 (12) : 18697 - 18710
  • [7] Information Magnitude Based Dynamic Sub-sampling for Speech-to-text
    Zhang, Yuhao
    Gao, Chenghao
    Kou, Kaiqi
    Xu, Chen
    Xiao, Tong
    Zhu, Jingbo
    INTERSPEECH 2023, 2023, : 4433 - 4437
  • [8] Exploring the Factors Aiding Speech-to-Text Emotional Restoration
    Chen, Xin
    Deng, Qingxin
    DESIGN, USER EXPERIENCE, AND USABILITY: DESIGN FOR DIVERSITY, WELL-BEING, AND SOCIAL DEVELOPMENT, DUXU 2021, PT II, 2021, 12780 : 420 - 433
  • [9] Speech-to-text intervention to support text production for students with intellectual disabilities
    Sand, Christina
    Svensson, Idor
    Nilsson, Staffan
    Selenius, Heidi
    Faelth, Linda
    DISABILITY AND REHABILITATION-ASSISTIVE TECHNOLOGY, 2025, 20 (02) : 408 - 415
  • [10] RECENT IMPROVEMENTS TO THE CAMBRIDGE ARABIC SPEECH-TO-TEXT SYSTEMS
    Tomalin, M.
    Diehl, F.
    Gales, M. J. F.
    Park, J.
    Woodland, P. C.
    2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, : 4382 - 4385